DocumentCode :
482412
Title :
Induction motor parameter determination technique using artificial neural networks
Author :
Karanayil, Baburaj ; Rahman, Muhammed Fazlur ; Grantham, Colin
Author_Institution :
Sch. of Electr. Eng. & Telecommun., Univ. of New South Wales, Sydney, NSW
fYear :
2008
fDate :
17-20 Oct. 2008
Firstpage :
793
Lastpage :
798
Abstract :
This paper presents a new method of on-line estimation for the stator and rotor resistances of the induction motor in the indirect vector controlled drive, using artificial neural networks. The back propagation algorithm is used for training of the neural networks. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. For the stator resistance estimation, the error between the measured stator current and the estimated stator current using neural network is back propagated to adjust the weights of the neural network. The performance of the stator and rotor resistance estimators and torque and flux responses of the drive, together with these estimators, are investigated with the help of simulations for variations in the stator and rotor resistances from their nominal values. Both resistances are estimated experimentally, using the proposed neural networks in a vector controlled induction motor drive. Data tracking performances of these estimators are presented. With this approach the rotor resistance estimation was found to be insensitive to the stator resistance variations both in simulation and experiment.
Keywords :
backpropagation; electric machine analysis computing; estimation theory; induction motor drives; matrix algebra; neural nets; artificial neural networks; back propagation algorithm; flux response; indirect vector controlled drive; induction motor parameter determination technique; on-line estimation; rotor flux linkages; rotor resistance estimation; stator resistance variation; torque response; voltage model; Artificial neural networks; Couplings; Current measurement; Electrical resistance measurement; Estimation error; Induction motors; Neural networks; Rotors; Stators; Voltage; artificial neural networks; induction motor drives; parameter identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Machines and Systems, 2008. ICEMS 2008. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-3826-6
Electronic_ISBN :
978-7-5062-9221-4
Type :
conf
Filename :
4770816
Link To Document :
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